We conduct OOD detection experiments on a wide range of datasets.
In each scenario, we train the model on the ID training set \(\mathcal {D}_\text{in}^{\text{train}}\) .
Later we evaluate the model on the ID testing set \(\mathcal {D}_\text{in}^{\text{test}}\)  and an OOD testing set \(\mathcal {D}_\text{out}^\text{test}\)  to see if the model can distinguish between ID and OOD examples.
Our experiments consist of three parts: (i) We follow the work in [1]} to fine-tune a simple two-layer GRU classifier [2]} using different methods.
(ii) Then we extend the evaluation to pre-trained language models (BERT) like [3]}. We report the OOD detection performance and illustrate the advantage of evidential uncertainty in (iii) the predictive uncertainty distribution.
